Beamforming is an advanced signal processing technique used in sensor arrays for directional signal transmission

or reception. The paper deals with a system based on an ultrasound transmitter and an array of

receivers, to determine the distance to an obstacle by measuring the time of flight and – using the phase

beamforming technique to process the output signals of receivers for finding the direction from which the

reflected signal is received – locates the obstacle. The embedded beam-former interacts with a PID-based

line follower robot to improve performance of the line follower navigation algorithm by detecting and

avoiding obstacles. The PID (proportional-integral-derivative) algorithm is also typically used to control

industrial processes. It calculates the difference between a measured value and a desired set of points, then

attempts to minimize the error by adjusting the output. The overall navigation system combines a PID-based

trajectory follower with a spatial-temporal filter (beamformer) that uses the output of an array of sensors to

extract signals received from an obstacle in a particular direction in order to guide an autonomous vehicle

or a robot along a safe path.

The article presents possible ways of development of decision-making processes in autonomous vehicles. The highest degree of autonomy means that it is not the driver but the system, machine or artificial intelligence that makes decisions about road activities. The total autonomy of vehicles gives them predictability, limits the number of accidents they cause, but also highlights the need to develop an ethical system that artificial intelligence will be able to refer to in a critical situation. It is not possible to foresee all the situations that will occur on the roads, so it is necessary to create robot- -human rights that will be a new and binding kind of decalogue. The key issue is that robotic-human rights should be universal, transparent and really applicable to everyone, otherwise there will be chaos on the road and the expected decrease of the number of accidents due to the introduction of autonomous vehicles will not come to pass.

Driver assistance systems have started becoming a key differentiator in automotive space and all major automotive manufacturers have such systems with various capabilities and stages of implementation. The main building blocks of such systems are similar in nature and one of the major building blocks is road lane detection. Even though lane detection technology has been around for decades, it is still an ongoing area of research and there are still several improvements and optimizations that are possible. This paper offers an Optimized Dynamic Origin Technique (Optimized DOT) for lane detection. The proposed optimization algorithm of optimized DOT gives better results in performance and accuracy compared to other methods of lane detection. Analysis of proposed optimized DOT with various edge detection techniques, various threshold levels, various sample dataset and various lane detection methods were done and the results are discussed in this paper. The proposed optimized DOT lane detection average processing time increases by 9.21 % when compared to previous Dynamic Origin Technique (DOT) and 59.09 % compared to traditional hough transform.